Data-driven RANS closures for three-dimensional flows around bluff bodies
| Autor: | Jasper P. Huijing, Martin Schmelzer, Richard P. Dwight |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science Reynolds averaged Navier-Stokes FOS: Physical sciences Computational fluid dynamics 01 natural sciences 010305 fluids & plasmas Physics::Fluid Dynamics symbols.namesake Sparse symbolic regression Incompressible flow 0103 physical sciences Machine learning Applied mathematics 0101 mathematics business.industry Data-driven modelling Fluid Dynamics (physics.flu-dyn) General Engineering Reynolds number Physics - Fluid Dynamics Computational Physics (physics.comp-ph) Solver 010101 applied mathematics Flow (mathematics) Closure (computer programming) symbols Symbolic regression Reynolds-averaged Navier–Stokes equations business Physics - Computational Physics |
| Zdroj: | Computers & Fluids, 225 |
| ISSN: | 0045-7930 |
| Popis: | In this short note we apply the recently proposed data-driven RANS closure modelling framework of Schmelzer et al. (2020) to fully three-dimensional, high Reynolds number flows: namely wall-mounted cubes and cuboids at Re=40,000, and a cylinder at Re=140,000. For each flow, a new RANS closure is generated using sparse symbolic regression based on LES or DES reference data. This new model is implemented in a CFD solver, and subsequently applied to prediction of the other flows. We see consistent improvements compared to the baseline $k-\omega$ SST model in predictions of mean-velocity in the complete flow domain. Comment: Submitted Computers & Fluids |
| Databáze: | OpenAIRE |
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